There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Kelsey Thomas |
| Institution: | University of California San Diego |
| Department: | Psychiatry |
| Country: | |
| Proposed Analysis: | Background: Work done in our lab highlights the clinical utility of using multiple neuropsychological measures to diagnose MCI when compared to more traditional methods of MCI diagnosis that may only include one impaired cognitive score (e.g., Bondi et al., 2014; Edmonds et al., 2015, 2016). Process scores (i.e., that provide information about the approach to completing a test) and error scores (i.e., that examine the types of errors made) are derived from neuropsychological measures and can be used to identify early patterns of cognitive difficulty or inefficiency prior to the emergence of frank impairment on the total score. Process (e.g., learning slope) and error (e.g., perseverations) scores are used in clinical neuropsychological practice; however, to our knowledge, this study is the first to examine process/error scores in a large longitudinal biomarker database like the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Improved characterization of these process/error scores and understanding their utility for early detection of a disease process and their relationship with biomarkers and functioning is critical in elevating our ability to predict those at risk for progression. Aim 1. Construct neuropsychological process/error scores and determine how these new scores are associated with cognitive, biomarker [cerebrospinal fluid (CSF), genetic] and functional profiles. Aim 1 Analysis: Descriptive analysis will characterize the neuropsychological process/error scores. Linear regressions, controlling for demographics, will examine the process/error scores and cognitive, biomarker, and functional relationships. Aim 2. Examine the added utility of process/error scores, above and beyond AD biomarkers, when predicting which cognitively normal (possible ‘preclinical AD’) participants progress to MCI. Aim 2 Analyses: Hierarchical logistic regression analysis that includes AD biomarkers (step 1) and process/error scores (step 2) to predict participant classification of either stably normal or progressed to MCI. Consistent with previous work normal participants will be characterized by summing the number of elevated AD biomarkers; analysis of variance will examine the differences in process/error scores by number of elevated biomarkers. Aim 3. Adapt our novel neuropsychological Jak/Bondi MCI criteria to include process/error scores and compare participant classification and outcomes with the ADNI traditional Petersen/Winblad MCI diagnostic criteria10,11 as well as the Jak/Bondi criteria that does not include the process/error scores. Aim 3 Analyses: Factor analysis will be used to determine which existing cognitive domains (e.g., memory, language, attention/executive) the process/error scores will be incorporated. Once the process/error scores have been integrated, examination of the participant overlap by classification criteria will be examined. Survival analyses will be used to examine diagnostic transitions and progression to dementia longitudinally. |
| Additional Investigators |
